import torch
import numpy as np
from torch import nn
from typing import Dict, List, Tuple, Union, Optional
from tianshou.policy import PGPolicy
from tianshou.data import Batch, ReplayBuffer, to_numpy, to_torch_as
[docs]class PPOPolicy(PGPolicy):
r"""Implementation of Proximal Policy Optimization. arXiv:1707.06347
:param torch.nn.Module actor: the actor network following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param torch.nn.Module critic: the critic network. (s -> V(s))
:param torch.optim.Optimizer optim: the optimizer for actor and critic
network.
:param torch.distributions.Distribution dist_fn: for computing the action.
:param float discount_factor: in [0, 1], defaults to 0.99.
:param float max_grad_norm: clipping gradients in back propagation,
defaults to ``None``.
:param float eps_clip: :math:`\epsilon` in :math:`L_{CLIP}` in the original
paper, defaults to 0.2.
:param float vf_coef: weight for value loss, defaults to 0.5.
:param float ent_coef: weight for entropy loss, defaults to 0.01.
:param action_range: the action range (minimum, maximum).
:type action_range: (float, float)
:param float gae_lambda: in [0, 1], param for Generalized Advantage
Estimation, defaults to 0.95.
:param float dual_clip: a parameter c mentioned in arXiv:1912.09729 Equ. 5,
where c > 1 is a constant indicating the lower bound,
defaults to 5.0 (set ``None`` if you do not want to use it).
:param bool value_clip: a parameter mentioned in arXiv:1811.02553 Sec. 4.1,
defaults to ``True``.
:param bool reward_normalization: normalize the returns to Normal(0, 1),
defaults to ``True``.
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(self,
actor: torch.nn.Module,
critic: torch.nn.Module,
optim: torch.optim.Optimizer,
dist_fn: torch.distributions.Distribution,
discount_factor: float = 0.99,
max_grad_norm: Optional[float] = None,
eps_clip: float = .2,
vf_coef: float = .5,
ent_coef: float = .01,
action_range: Optional[Tuple[float, float]] = None,
gae_lambda: float = 0.95,
dual_clip: Optional[float] = None,
value_clip: bool = True,
reward_normalization: bool = True,
**kwargs) -> None:
super().__init__(None, None, dist_fn, discount_factor, **kwargs)
self._max_grad_norm = max_grad_norm
self._eps_clip = eps_clip
self._w_vf = vf_coef
self._w_ent = ent_coef
self._range = action_range
self.actor = actor
self.critic = critic
self.optim = optim
self._batch = 64
assert 0 <= gae_lambda <= 1, 'GAE lambda should be in [0, 1].'
self._lambda = gae_lambda
assert dual_clip is None or dual_clip > 1, \
'Dual-clip PPO parameter should greater than 1.'
self._dual_clip = dual_clip
self._value_clip = value_clip
self._rew_norm = reward_normalization
[docs] def process_fn(self, batch: Batch, buffer: ReplayBuffer,
indice: np.ndarray) -> Batch:
if self._rew_norm:
mean, std = batch.rew.mean(), batch.rew.std()
if not np.isclose(std, 0):
batch.rew = (batch.rew - mean) / std
if self._lambda in [0, 1]:
return self.compute_episodic_return(
batch, None, gamma=self._gamma, gae_lambda=self._lambda)
v_ = []
with torch.no_grad():
for b in batch.split(self._batch, shuffle=False):
v_.append(self.critic(b.obs_next))
v_ = to_numpy(torch.cat(v_, dim=0))
return self.compute_episodic_return(
batch, v_, gamma=self._gamma, gae_lambda=self._lambda)
[docs] def forward(self, batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
**kwargs) -> Batch:
"""Compute action over the given batch data.
:return: A :class:`~tianshou.data.Batch` which has 4 keys:
* ``act`` the action.
* ``logits`` the network's raw output.
* ``dist`` the action distribution.
* ``state`` the hidden state.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
logits, h = self.actor(batch.obs, state=state, info=batch.info)
if isinstance(logits, tuple):
dist = self.dist_fn(*logits)
else:
dist = self.dist_fn(logits)
act = dist.sample()
if self._range:
act = act.clamp(self._range[0], self._range[1])
return Batch(logits=logits, act=act, state=h, dist=dist)
[docs] def learn(self, batch: Batch, batch_size: int, repeat: int,
**kwargs) -> Dict[str, List[float]]:
self._batch = batch_size
losses, clip_losses, vf_losses, ent_losses = [], [], [], []
v = []
old_log_prob = []
with torch.no_grad():
for b in batch.split(batch_size, shuffle=False):
v.append(self.critic(b.obs))
old_log_prob.append(self(b).dist.log_prob(
to_torch_as(b.act, v[0])))
batch.v = torch.cat(v, dim=0) # old value
batch.act = to_torch_as(batch.act, v[0])
batch.logp_old = torch.cat(old_log_prob, dim=0)
batch.returns = to_torch_as(
batch.returns, v[0]).reshape(batch.v.shape)
if self._rew_norm:
mean, std = batch.returns.mean(), batch.returns.std()
if not np.isclose(std.item(), 0):
batch.returns = (batch.returns - mean) / std
batch.adv = batch.returns - batch.v
if self._rew_norm:
mean, std = batch.adv.mean(), batch.adv.std()
if not np.isclose(std.item(), 0):
batch.adv = (batch.adv - mean) / std
for _ in range(repeat):
for b in batch.split(batch_size):
dist = self(b).dist
value = self.critic(b.obs)
ratio = (dist.log_prob(b.act) - b.logp_old).exp().float()
surr1 = ratio * b.adv
surr2 = ratio.clamp(
1. - self._eps_clip, 1. + self._eps_clip) * b.adv
if self._dual_clip:
clip_loss = -torch.max(torch.min(surr1, surr2),
self._dual_clip * b.adv).mean()
else:
clip_loss = -torch.min(surr1, surr2).mean()
clip_losses.append(clip_loss.item())
if self._value_clip:
v_clip = b.v + (value - b.v).clamp(
-self._eps_clip, self._eps_clip)
vf1 = (b.returns - value).pow(2)
vf2 = (b.returns - v_clip).pow(2)
vf_loss = .5 * torch.max(vf1, vf2).mean()
else:
vf_loss = .5 * (b.returns - value).pow(2).mean()
vf_losses.append(vf_loss.item())
e_loss = dist.entropy().mean()
ent_losses.append(e_loss.item())
loss = clip_loss + self._w_vf * vf_loss - self._w_ent * e_loss
losses.append(loss.item())
self.optim.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(list(
self.actor.parameters()) + list(self.critic.parameters()),
self._max_grad_norm)
self.optim.step()
return {
'loss': losses,
'loss/clip': clip_losses,
'loss/vf': vf_losses,
'loss/ent': ent_losses,
}